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-# Multilingual Sentiment Analysis
-
-An end-to-end NLP project for three-class sentiment classification: **negative**, **neutral**, and **positive**. The repository contains two XLM-RoBERTa experiments:
-
-1. an archived English airline-tweet model, and
-2. the current general multilingual Twitter-sentiment model.
-
-The current model is exposed through a Gradio app and is evaluated both overall and separately for every supported language.
-
-## 🎮 Demo: Gradio App
-
-Run the interactive demo locally:
-```bash
-python app.py
-```
-
-![Gradio app batch-analysis demo](assets/demo-screenshot.png)
-
-## Results at a glance
-
-| Experiment | Data | Evaluation | Result |
-| --- | --- | --- | --- |
-| Airline tweets (legacy) | English airline tweets | Validation set | 84.24% accuracy, 0.842 weighted F1 |
-| General multilingual (current) | 8-language Twitter sentiment dataset | Held-out test set (6,960 tweets) | 66.52% accuracy, 0.663 macro F1 |
-
-The two scores are **not directly comparable**: the airline model was evaluated on a narrow, English-only domain, whereas the multilingual model is evaluated across eight languages and broader Twitter content.
-
-### Current multilingual model: confusion matrix
-
-<img src="results/multilingual/eval/confusion_matrix.png" width="500">
-
-### Airline-tweet model: evaluation plots
-
-<img src="results/airline_tweets/eval/confusion_matrix.png" width="400">
-
-<img src="results/airline_tweets/eval/loss_curve.png" width="400">
-
-
-### Current multilingual model: test macro F1 by language
-
-| Language | Macro F1 |
-| --- | ---: |
-| German | 0.731 |
-| French | 0.721 |
-| Portuguese | 0.699 |
-| English | 0.692 |
-| Spanish | 0.667 |
-| Arabic | 0.647 |
-| Italian | 0.625 |
-| Hindi | 0.516 |
-
-
-The neutral class is the most difficult overall (0.588 F1). Hindi is the weakest supported language, so this project does not claim equal quality across all languages.
-
-## How it works
-
-1. `multilingual_sentiment_analysis.preprocess` downloads and prepares the multilingual dataset.
-2. Text is cleaned by removing URLs and mentions, normalizing whitespace, and removing only the `#` marker. Unicode text, accents, emojis, and non-Latin scripts are preserved.
-3. `multilingual_sentiment_analysis.train` fine-tunes `xlm-roberta-base` for three-class sequence classification.
-4. `multilingual_sentiment_analysis.evaluate` reports overall metrics, per-language metrics, and a confusion matrix.
-5. `app.py` provides single-text and batch inference with the saved model.
-
-## Dataset
-
-The current experiment uses [Cardiff NLP's Tweet Sentiment Multilingual dataset](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual). It provides the same label mapping used by this project:
-
-```text
-0 = negative
-1 = neutral
-2 = positive
-```
-
-It includes Arabic, English, French, German, Hindi, Italian, Portuguese, and Spanish. The preparation script loads each language configuration separately so it can preserve the official train/validation/test split and retain the language for evaluation.
-
-## Repository layout
-
-```text
-src/
-  multilingual_sentiment_analysis/
-    config.py       # shared paths, labels, and dataset settings
-    preprocess.py   # dataset download, cleaning, tokenization, and saving
-    train.py        # fine-tuning entry point
-    evaluate.py     # test and per-language evaluation
-    infer.py        # lazy model loading and inference helpers
-tests/            # regression tests for UI and preprocessing behavior
-data/
-  airline_tweets/ # archived English-only source data and prepared splits
-  multilingual/   # prepared multilingual splits
-results/
-  airline_tweets/ # archived English-only checkpoints
-  multilingual/   # current checkpoints and evaluation outputs
-sentiment_model/
-  airline-tweets-sentiment-model/         # archived English-only model
-  general-multilingual-sentiment-model/   # current app model
-```
-
-Data, checkpoints, and model weights are intentionally ignored by Git.
-
-
-## Training configuration
-
-- Base checkpoint: `xlm-roberta-base`
-- Maximum token length: 128
-- Epochs: 3
-- Learning rate: 2e-5 with the Trainer's linear schedule
-- Train/evaluation batch sizes: 16 / 32
-- Best checkpoint criterion: weighted F1 on the validation set
-- Random seed: 42
-
-The multilingual training run completed in approximately 4 minutes 40 seconds on an NVIDIA RTX 5060 Laptop GPU.
-
-## Limitations and next steps
-
-- The model was evaluated on Twitter-style text only; it is not validated for reviews, support tickets, or other domains.
-- It is evaluated for eight languages, not all languages supported by the XLM-R tokenizer.
-- Hindi and neutral-sentiment performance require further improvement.
-- A useful next experiment is to initialize from a Twitter-adapted multilingual encoder such as XLM-T and compare per-language macro F1 against this baseline.
-
-## Testing
-
-```powershell
-python -m pytest -q
-```
-
-## Attribution
-
-The multilingual dataset is provided by Cardiff NLP. See its [dataset card](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) and the associated [XLM-T paper](https://aclanthology.org/2022.lrec-1.27/).